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研究生:吳國銘
研究生(外文):Kuo-Ming Wu
論文名稱:直序展頻CDMA無線通訊系統之干擾抑制技術
論文名稱(外文):Interference Suppression Techniques for DS-CDMA Wireless Communications
指導教授:王晉良
指導教授(外文):Chin-Liang Wang
學位類別:博士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:313
中文關鍵詞:展頻通訊直序展頻分碼多工進接窄頻干擾抑制多用戶偵測干擾去除渦輪處理順向錯誤更正碼功率控制
外文關鍵詞:spread spectrum communicationcode division multiple accessnarrowband interference suppressionmultiuser detectioninterference cancellationturbo processingforward error correctionpower control
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為滿足未來無線行動通訊系統中高容量及多樣化的服務需求,直序展頻分碼多工進接(DS-CDMA)是相當被看好的一種技術。而 DS-CDMA 系統的性能,往往受限於系統中的種種干擾;這些干擾可分成兩大類:窄頻干擾與寬頻干擾;窄頻干擾乃由與 DS-CDMA 系統頻帶相疊的窄頻通訊系統所造成,而寬頻干擾則包含了 DS-CDMA 系統中其他用戶所帶來的多重存取干擾(MAI)以及由於各用戶之接收功率不均所導致的遠近效應(near-far effect)。這些問題將會大大降低了 DS-CDMA 系統的效能。在本論文中,我們致力於 DS-CDMA 系統中干擾抑制技術之研究,我們將著重於四項核心技術:窄頻干擾消除、多用戶偵測、順向錯誤更正碼以及功率控制。
窄頻干擾消除技術能將 DS-CDMA 信號與外來之窄頻信號分離,因此對系統效能有著深遠的影響,尤其當干擾很強烈的時候。過去在此方面的作法不是性能不佳就是複雜度太高。我們提出了一個新式的窄頻干擾消除法,此方法為一非線性的適應式窄頻干擾估測器,其最大特徵是能對估測的結果作迴授式的補償,以增加估測的準確度。與近似最佳化的架構比較,我們所提出的方法有著相近的效能,卻只需要較低的運算複雜度。
在 DS-CDMA 系統中,為解決各用戶接收信號時所受到的多重存取干擾,多用戶偵測技術將扮演著關鍵的角色;在眾多多用戶偵測器的作法中,多級平行干擾消除法(PIC)由於有著簡單的結構及不錯的干擾消除能力,因而備受矚目。我們提出了一低複雜度的多級渦輪式部分平行干擾消除(TPPIC)偵測器,在每一消除級中,所發展的新架構能輸出外部可靠度訊息,此一訊息並可輸入至下一級中當作事先可靠度訊息來使用,這過程就如同威力強大的渦輪處理方式。我們亦針對一些關鍵的參數,探討 TPPIC 偵測器之特性,並將 TPPIC 多用戶偵測法與適應式濾波技術整合,以更進一步提升其效能。經由模擬結果顯示,我們所提的 TPPIC 架構,其在位元錯誤率的表現優於以往許多知名的多用戶偵測器。
藉由不同接收器組件間反覆式的軟式訊息交換,可以提升整體系統的性能。我們將此一觀念應用在結合多用戶偵測以及通道解碼的設計上;由於在第三代無線通訊系統的標準書中,已選擇渦輪碼為反向通道之編碼方式,我們將著重於 TPPIC 多用戶偵測器以及渦輪碼解碼系統的綜合設計演算法。我們提出了一低複雜度的反覆式多用戶接收器,在此一接收器中,TPPIC 偵測器能提供軟式輸出予渦輪解碼器,並接收來自渦輪解碼器的軟式訊息,此一反覆式的訊息交換有助於提升估測位元值的可靠度。我們並將此一架構與與適應式濾波技術整合,以更進一步加強其效能。經由廣泛的電腦模擬顯示,我們所提的反覆式接收器有著遠優於傳統非反覆式接收器的表現,其性能亦逼近於一些高複雜度、近似最佳化的多用戶接收器。
功率控制是一種用來克服遠近效應的方法,它是透過平衡各用戶間的接收功率,使得任一用戶皆不能對其他用戶造成過度的干擾。為了能有效率地對抗遠近效應,我們將功率控制與多用戶偵測技術結合,藉由多用戶偵測器對多重存取干擾的抑制能力及其輸出端較佳的信號品質,有效地調整發射端之功率,以達到最經濟的總體發射功率值;此外,針對功率控制所必要之參數,我們亦提出了一新的估測方法。模擬數據顯示,我們所提出的功率控制技術,能在遠近效應嚴重的環境中,有效地達到所要求的通訊品質。
Future wireless personal and mobile communication systems are expected to provide high-capacity flexible services. Direct-sequence code-division multiple-access (DS-CDMA) has emerged as a promising candidate to meet these challenges. It is well known that performance of DS-CDMA systems is interference-limited, where factors lead to such an obstruction can be classified into two categories. One is the narrowband interference (NBI) resulting from external overlaid narrowband services and the other is the wideband interferences, which include the multiple access interference (MAI) incurred from other co-channel CDMA users and the near-far effect originated with an unbalanced received power arrangement among the users. In this thesis, advanced receiver techniques capable of suppressing these interferences are considered through NBI suppression, multiuser detection, forward error correction (FEC), and power control.
NBI suppression techniques play an important role in separating a DS-CDMA multiplex and the external narrowband jamming and hence heavily affect the performance of a CDMA system especially when the NBI is strong. Most of the existing methods on this issue either perform unsatisfactorily or involve high complexity. We present a new approach for NBI suppression in a DS-CDMA system. The proposed scheme is an adaptive nonlinear predictor with a novel feedback compensation for the predicted result. This scheme achieves comparable performance to the nearly optimal approach but involves much lower complexity.
Multiuser detection techniques are essential to guarantee acceptable performance in DS-CDMA systems where signals conveying the desired information are received in the presence of MAI. Multiuser detection using multistage parallel interference cancellation (PIC) is particularly attractive owing to its simple structure and potential interference cancellation capability. We propose a low-complexity multistage turbo partial PIC (TPPIC) detector for DS-CDMA systems. At each stage of TPPIC, extrinsic information is extracted and then used as the a priori information for the next stage, just as the manner of powerful turbo processing. We also explore the properties of TPPIC with respect to some essential parameters and consider the incorporation of adaptive linear filtering techniques for further performance enhancement. The proposed TPPIC detector exhibits an impressive performance improvement over many existing multiuser detection schemes.
Soft-information exchange between different receiver sub-modules has been demonstrated to give good performance. This motivated us to consider joint multiuser detection and FEC decoding in a DS-CDMA system. We focus on the joint design of the TPPIC-based multiuser detection and the turbo decoding algorithm, since turbo codes have been chosen as one of the coding schemes for the forthcoming third-generation wireless communication system. We propose a low-complexity iterative multiuser receiver based on TPPIC for a turbo-coded DS-CDMA system. This structure is further elaborated for performance reinforcement by incorporating the adaptive interference suppression techniques. The performance of the proposed iterative receiver is significantly better than that of the traditional non-iterative receivers and is close to that of some well-known iterative schemes of much higher complexity.
Power control is aimed to mitigate the deleterious near-far effect and refers to a resource allocation technique that balances the received powers of the users so that no user creates excessive interference. To effectively overcome the near-far effect, we implement power control with various multiuser detection algorithms discussed in this thesis and present a new method for estimating the required parameter of power control. Extensive numerical results reveal that the proposed power control strategy is effective to attain a given quality of service in a near-far environment.
Contents
Abstract i
Contents iii
List of Figures ix
List of Tables xxix
Chapter 1 Introduction
1.1 Code-Division Multiple-Access Techniques 1
1.2 Limitation Factors of the DS-CDMA System Performance 4
1.2.1 The Narrowband Disturbance 4
1.2.2 The Wideband Disturbance 5
1.3 Aim and Outline of the Thesis 7
1.4 Multiuser Communication Model 9
1.4.1 Basic Received Waveforms 9
1.4.2 Signal Model in the Absence of NBI 10
1.4.2.1 Signal Model in Nonfading Synchronous Channels 10
1.4.2.2 Signal Model in Nonfading Asynchronous Channels 13
1.4.2.3 Signal Model in Fading Dispersive Channels 18
Chapter 2 Narrowband Interference Suppression for DS-CDMA Systems
2.1 Preliminaries 32
2.2 Description of the System 34
2.3 A New Narrowband Interference Suppression Scheme 36
2.3.1 The Improved Offset Predictor Structure 36
2.3.1.1 Derivation of a New Offset Compensation Scheme 36
2.3.1.2 Nonlinear Predictor with the New Offset Compensation Scheme 39
2.3.1.3 Complexity Comparison with the Near-Optimum ACM Predictor 41
2.3.2 Simulation Results 42
2.3.3 Robustness Investigation against Other Types of Narrowband Interference 44
2.3.4 Discussions 45
2.4 A Modified Approach with Simplification in Offset Compensation 46
2.4.1 Derivation of the Modified Offset Predictor 46
2.4.2 Simulation Results 48
2.4.3 Discussions 49
2.5 Summary 49
Chapter 3 Multiuser Detection for DS-CDMA Systems
3.1 Preliminaries 66
3.2 Description of the System 68
3.3 A New Parallel Interference Cancellation Scheme for DS-CDMA Communications 70
3.3.1 Previous Works on Parallel Interference Cancellation 70
3.3.1.1 The Conventional PIC (Total-PIC) 70
3.3.1.2 The Conventional PPIC 71
3.3.1.3 The Linear PPIC 71
3.3.1.4 The Adaptive Least-Mean-Square PIC (LMS-PIC) 72
3.3.2 The Turbo Partial Parallel Interference Cancellation Strategy 73
3.3.3 Multipath Considerations 78
3.3.4 Simulation Results 80
3.3.4.1 Performance Comparison with Other Schemes 81
3.3.4.2 Robustness Examination of the TPPIC Detector 83
3.3.4.3 Performance Evaluations in Multipath Fading Environments 84
3.3.5 Discussions 85
3.4 Improved TPPIC Detectors for DS-CDMA Systems 86
3.4.1 The TPPIC Detector with Enhanced Variance Estimation 87
3.4.2 The TPPIC Detector with Optimized Cancellation Weights 88
3.4.3 Simulation Results 90
3.4.4 Discussions 94
3.5 Combined Parallel Interference Cancellation and Adaptive Minimum Mean-Squared Error Filtering for DS-CDMA Systems 94
3.5.1 The TPPIC/AMMSE Multiuser Detection 96
3.5.2 Multipath Considerations 100
3.5.3 Simulation Results 101
3.5.3.1 Performance Comparison with Other Schemes 102
3.5.3.2 Robustness Examination of the TPPIC/AMMSE Detector 105
3.5.3.3 Performance Evaluations in Multipath Fading Environments 105
3.5.4 The TPPIC/AMMSE Approach with Further Computational Reduction 107
3.5.4.1 Derivation of the Reduced-Complexity Scheme 107
3.5.4.2 Simulation Results 109
3.5.5 Discussions 111
3.6 Summary 111
Chapter 4 Joint Multiuser Detection and Forward Error Correction for DS-CDMA Systems
4.1 Preliminaries 144
4.2 Description of the System 147
4.3 An Iterative Multiuser Receiver Using TPPIC for Turbo-Coded DS-CDMA Systems 149
4.3.1 The Iterative TPPIC Receiver Structure 149
4.3.1.1 Modification of the TPPIC Algorithm 149
4.3.1.2 An Iterative Turbo-Coded DS-CDMA Receiver 150
4.3.2 Simulation Results 152
4.3.2.1 Performance Comparison with Other Schemes 153
4.3.2.2 Investigation of the Near-Far Resistance 156
4.3.2.3 Robustness Examination against Noise Variance Estimations 156
4.3.3 Multipath Considerations 157
4.3.3.1 Extension of the Iterative TPPIC Receiver 158
4.3.3.2 Performance Evaluations in Multipath Channels 158
4.3.4 Discussions 159
4.4 Generalization of the Iterative Multiuser Receiver for Suppressing Unknown Multiple Access Interference 160
4.4.1 The Iterative TPPIC/AMMSE Receiver Structure 160
4.4.2 Simulation Results 162
4.4.2.1 Performance Comparison with Other Schemes 163
4.4.2.2 Investigation of the Near-Far Resistance 164
4.4.2.3 Robustness Examination against Noise Variance Estimations 165
4.4.3 Multipath Considerations 165
4.4.3.1 Extension of the Iterative TPPIC/AMMSE Receiver 165
4.4.3.2 Performance Evaluations in Multipath Channels 166
4.4.4 Discussions 167
4.5 Summary 168
Chapter 5 Power Control for DS-CDMA Systems
5.1 Preliminaries 190
5.2 Description of the System 192
5.3 A Power Control Scheme Using Adaptive Minimum Mean-Squared Error Filtering 194
5.3.1 Previous Work on Power Control with MMSE Filtering 194
5.3.2 A New Power Control Scheme with Adaptive Interference Suppression 196
5.3.2.1 The Proposed Interference Suppression Strategy 196
5.3.2.2 A New Parameter Estimation Method 198
5.3.3 Simulation Results 200
5.3.4 Discussions 206
5.4 A Power Control Scheme Using Turbo Partial Parallel Interference Cancellation 206
5.4.1 Previous Work on Power Control with Parallel Interference Cancellation 207
5.4.2 The Proposed Power Control Strategy Using TPPIC 209
5.4.3 Simulation Results 210
5.4.4 Multipath Considerations 214
5.4.4.1 Extension of the Proposed Power Control Algorithms 214
5.4.4.2 Performance Evaluations in Multipath Channels 214
5.4.5 Discussions 217
5.5 A Power Control Scheme Using Combined Parallel Interference Cancellation and Minimum Mean-Squared Error Filtering 217
5.5.1 A Combined TPPIC/AMMSE Power Control Scheme 218
5.5.2 Simulation Results 219
5.5.3 Multipath Considerations 222
5.5.3.1 Extension of the TPPIC/AMMSE Power Control Approach 222
5.5.3.2 Performance Evaluations in Multipath Channels 222
5.5.4 Discussions 225
5.6 Power Control in NBI-Existed Environments 225
5.6.1 Power Control Algorithms with NBI Suppression 226
5.6.2 Simulation Results 226
5.6.3 Discussions 228
5.7 Summary 229
Chapter 6 Conclusions
6.1 Summary of Results 279
6.2 Suggestions for Future Study 283
Bibliography 287
Publication List 311
List of Figures
Fig. 1.1 Spectra of desired received signal and NBI before and after despreading. 23
Fig. 1.2. K-user DS-CDMA communication system model (the channels account for multipath effects, asynchronism, and attenuation of the transmitted signals). 24
Fig. 1.3. Structure of the matched-filter bank for synchronous DS-CDMA communications. 25
Fig. 1.4. Illustration of users’ relative timings for one-shot matched filtering. 26
Fig. 1.5. Structure of the one-shot matched-filter bank for asynchronous DS-CDMA communications. 27
Fig. 1.6. Illustration of users’ relative timings for multi-shot matched filtering. 28
Fig. 1.7. Structure of the multi-shot matched-filter bank for asynchronous DS-CDMA communications. 29
Fig. 2.1. Improved offset predictor for NBI suppression. 51
Fig. 2.2. Performance comparison of several NBI suppression schemes with L=10 for the one-user case. 52
Fig. 2.3. Performance comparison of several NBI suppression schemes with L=10 for the ten-user case. 52
Fig. 2.4. Performance comparison of several NBI suppression schemes with L=10 for the 25-user case. 53
Fig. 2.5. Performance comparison of several NBI suppression schemes with L=10 for the 50-user case. 53
Fig. 2.6. Simulation results summary of the improved offset predictor for several cases (L=10).
54
Fig. 2.7 Performance investigation of the improved offset predictor with reduced filter length for the one-user case. 55
Fig. 2.8. Performance investigation of the improved offset predictor with reduced filter length for the ten-user case. 55
Fig. 2.9. Performance investigation of the improved offset predictor with reduced filter length for the 25-user case. 56
Fig. 2.10. Performance investigation of the improved offset predictor with reduced filter length for the 50-user case. 56
Fig. 2.11. Performance investigation of the improved offset predictor under the pure tone jamming (one-user case). 57
Fig. 2.12. Performance investigation of the improved offset predictor under the pure tone jamming (ten-user case). 57
Fig. 2.13. Performance investigation of the improved offset predictor under the pure tone jamming (25-user case). 58
Fig. 2.14. Performance investigation of the improved offset predictor under the pure tone jamming (50-user case). 58
Fig. 2.15. Performance investigation of the improved offset predictor under the chirped jamming (one-user case). 59
Fig. 2.16. Performance investigation of the improved offset predictor under the chirped jamming (ten-user case). 59
Fig. 2.17. Performance investigation of the improved offset predictor under the chirped jamming (25-user case). 60
Fig. 2.18. Performance investigation of the improved offset predictor under the chirped jamming (50-user case). 60
Fig. 2.19. Modified offset predictor with simplification in output compensation. 61
Fig. 2.20. Performance comparison of the modified offset predictor to other schemes for the one-user case. 62
Fig. 2.21. Performance comparison of the modified offset predictor to other schemes for the ten-user case. 62
Fig. 2.22. Performance comparison of the modified offset predictor to other schemes for the 25-user case. 63
Fig. 2.23. Performance comparison of the modified offset predictor to other schemes for the 50-user case. 63
Fig. 3.1. mth stage of the proposed TPPIC multiuser detector. 114
Fig. 3.2. Performance comparison of various multiuser detection schemes as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 115
Fig. 3.3. Performance comparison of various multiuser detection schemes as a function of the number of users in a power-balanced DS-CDMA system. ( , , ) 115
Fig. 3.4. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-balanced DS-CDMA system. ( , , ) 116
Fig. 3.5. Performance comparison of various multiuser detection schemes as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 116
Fig. 3.6. Robustness investigation of the TPPIC detector against various extents of amplitude estimation errors. ( , , ) 117
Fig. 3.7. Robustness investigation of the TPPIC detector against various extents of phase estimation errors. ( , , ) 117
Fig. 3.8. Robustness investigation of the TPPIC detector against various extents of timing estimation errors. ( , , ) 118
Fig. 3.9. Performance comparison of various multiuser detection schemes as a function of SNR in a two-path Rayleigh fading channel. ( , , ) 119
Fig. 3.10. Performance comparison of the primitive TPPIC detector and the TPPIC-EVE approach with different variance estimation methods as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 120
Fig. 3.11. Performance comparison of the primitive TPPIC detector and the TPPIC-EVE approach with different variance estimation methods as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 120
Fig. 3.12. Performance comparison of the primitive TPPIC detector and the TPPIC-EVE approach with different variance estimation methods as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 121
Fig. 3.13. Performance comparison of the primitive TPPIC detector and the TPPIC-EVE approach with different variance estimation methods as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 121
Fig. 3.14. Performance comparison of the primitive TPPIC detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 122
Fig. 3.15. Performance comparison of the primitive TPPIC detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 122
Fig. 3.16. Performance comparison of the primitive TPPIC detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 123
Fig. 3.17. Performance comparison of the primitive TPPIC detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 123
Fig. 3.18. Performance comparison of the TPPIC-EVE detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 124
Fig. 3.19. Performance comparison of the TPPIC-EVE detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 124
Fig. 3.20. Performance comparison of the TPPIC-EVE detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 125
Fig. 3.21. Performance comparison of the TPPIC-EVE detector with various selections of cancellation weights and the TPPIC-OPF approach as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 125
Fig. 3.22. Performance of the conventional PPIC detector with various selections of cancellation weights as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 126
Fig. 3.23. Performance of the conventional PPIC detector with various selections of cancellation weights as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 126
Fig. 3.24. Performance of the conventional PPIC detector with various selections of cancellation weights as a function of SNR in a power-balanced DS-CDMA system. ( , , ) 127
Fig. 3.25. Performance of the conventional PPIC detector with various selections of cancellation weights as a function of SNR in a power-unbalanced DS-CDMA system. ( , , ) 127
Fig. 3.26. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-balanced DS-CDMA system. ( , , ) 128
Fig. 3.27. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-unbalanced DS-CDMA system. ( , for the weakest user, ) 128
Fig. 3.28. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-balanced DS-CDMA system. ( , , ) 129
Fig. 3.29. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-unbalanced DS-CDMA system. ( , for the weakest user, ) 129
Fig. 3.30. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-balanced DS-CDMA system. ( , , ) 130
Fig. 3.31. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-unbalanced DS-CDMA system. ( , for the weakest user, ) 130
Fig. 3.32. mth stage of the proposed TPPIC/AMMSE multiuser detector. 131
Fig. 3.33. Performance comparison of various multiuser detection schemes as a function of SNR in a power-balanced DS-CDMA system with unidentified users. ( , ) 132
Fig. 3.34. Performance comparison of various multiuser detection schemes as a function of SNR in a power-unbalanced DS-CDMA system with unidentified users. ( , ) 132
Fig. 3.35. Performance comparison of various multiuser detection schemes as a function of the power offset in a power-balanced DS-CDMA system with unidentified users. ( , , ) 133
Fig. 3.36. Performance comparison of various multiuser detection schemes as a function of the power offset in a power-unbalanced DS-CDMA system with unidentified users. ( , for the weakest known user, ) 133
Fig. 3.37. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-balanced DS-CDMA system with unidentified users. ( , , ) 134
Fig. 3.38. Performance comparison of various multiuser detection schemes as a function of the number of cancellation stages in a power-unbalanced DS-CDMA system with unidentified users. ( , for the weakest known user, ) 134
Fig. 3.39. Robustness investigation of the TPPIC/AMMSE detector against various extents of amplitude estimation errors. ( , , ) 135
Fig. 3.40. Robustness investigation of the TPPIC/AMMSE detector against various extents of phase estimation errors. ( , , ) 135
Fig. 3.41. Robustness investigation of the TPPIC/AMMSE detector against various extents of timing estimation errors. ( , , ) 136
Fig. 3.42. Robustness investigation of the TPPIC/AMMSE detector against various selections of cancellation weights. ( , , ) 136
Fig. 3.43. Performance comparison of various multiuser detection schemes as a function of SNR in a two-path Rayleigh fading channel with existence of unidentified users. ( , , ) 137
Fig. 3.44. mth stage of the simplified TPPIC/AMMSE multiuser detector. 138
Fig. 3.45. Performance comparison of the simplified and the original TPPIC/AMMSE detectors with different adaptive algorithms as a function of SNR in a power-balanced DS-CDMA system with unidentified users. ( , , ) 139
Fig. 3.46. Performance comparison of the simplified and the original TPPIC/AMMSE detectors with different adaptive algorithms as a function of SNR in a power-unbalanced DS-CDMA system with unidentified users. ( , , ) 139
Fig. 3.47. Performance comparison of the simplified and the original TPPIC/AMMSE detectors with different adaptive algorithms as a function of the power offset in a power-balanced DS-CDMA system with unidentified users. ( , , , ) 140
Fig. 3.48. Performance comparison of the simplified and the original TPPIC/AMMSE detectors with different adaptive algorithms as a function of the power offset in a power-unbalanced DS-CDMA system with unidentified users. ( , for the weakest known user, , ) 140
Fig. 4.1. A rate 1/3 turbo-code encoder with trellis termination. 170
Fig. 4.2. mth stage of the TPPIC multiuser detector with the a priori reliability information generated from other receiver sub-modules. 171
Fig. 4.3. The proposed iterative TPPIC multiuser receiver for turbo-coded DS-CDMA systems.
172
Fig. 4.4. Block diagram of the turbo decoder for user k. 173
Fig. 4.5. Performance comparison of various multiuser receivers as a function of SNR in a power-balanced turbo-coded DS-CDMA system. ( , ) 174
Fig. 4.6. Performance comparison of various multiuser receivers as a function of SNR in a power-balanced turbo-coded DS-CDMA system. ( , ) 174
Fig. 4.7. Performance comparison of various multiuser receivers as a function of SNR in a power-balanced turbo-coded DS-CDMA system. ( , ) 175
Fig. 4.8. Performance comparison of various multiuser receivers as a function of the number of receiver iterations in a power-balanced DS-CDMA system. ( , , ) 175
Fig. 4.9. Near-far resistance investigation of the iterative TPPIC receiver. (User #1, , , ) 176
Fig. 4.10. Near-far resistance investigation of the iterative TPPIC receiver. (User #2, , , ) 176
Fig. 4.11. Near-far resistance investigation of the iterative TPPIC receiver. (User #3, , , ) 177
Fig. 4.12. Near-far resistance investigation of the iterative TPPIC receiver. (User #4, , , ) 177
Fig. 4.13. Robustness investigation of the iterative TPPIC receiver against various extents of estimation errors. (Underestimations, , ) 178
Fig. 4.14. Robustness investigation of the iterative TPPIC receiver against various extents of estimation errors. (Overestimations, , ) 178
Fig. 4.15. Performance comparison of different multiuser receivers as a function of SNR in a multipath channel. ( , , ) 179
Fig. 4.16. mth stage of the TPPIC/AMMSE multiuser detector with the a priori reliability information . 180
Fig. 4.17. The proposed iterative TPPIC/AMMSE multiuser receiver for turbo-coded DS-CDMA systems. 181
Fig. 4.18. Performance comparison of various multiuser receivers as a function of SNR in a power-balanced turbo-coded DS-CDMA system with an unidentified user. ( , , ) 182
Fig. 4.19. Performance comparison of various multiuser receivers as a function of SNR in a power-balanced turbo-coded DS-CDMA system with an unidentified user. ( , , ) 182
Fig. 4.20. Performance comparison of various multiuser receivers as a function of SNR in a power-balanced turbo-coded DS-CDMA system with an unidentified user. ( , , ) 183
Fig. 4.21. Performance comparison of various multiuser receivers as a function of the number of receiver iterations in a power-balanced DS-CDMA system with an unidentified user. ( , , , ) 183
Fig. 4.22. Near-far resistance investigation of the iterative TPPIC/AMMSE receiver in a DS-CDMA system with an unidentified user. (User #1, , , , ) 184
Fig. 4.23. Near-far resistance investigation of the iterative TPPIC/AMMSE receiver in a DS-CDMA system with an unidentified user. (User #2, , , , ) 184
Fig. 4.24. Near-far resistance investigation of the iterative TPPIC/AMMSE receiver in a DS-CDMA system with an unidentified user. (User #3, , , , ) 185
Fig. 4.25. Near-far resistance investigation of the iterative TPPIC/AMMSE receiver in a DS-CDMA system with an unidentified user. (User #4, , , , ) 185
Fig. 4.26. Robustness investigation of the iterative TPPIC/AMMSE receiver against various extents of estimation errors in a power-balanced DS-CDMA system with an unidentified user. (Underestimations, , , ) 186
Fig. 4.27. Robustness investigation of the iterative TPPIC/AMMSE receiver against various extents of estimation errors in a power-balanced DS-CDMA system with an unidentified user. (Overestimations, , , ) 186
Fig. 4.28. Performance comparison of different multiuser receivers as a function of SNR in a multipath channel with existence of unidentified users. ( , , , ) 187
Fig. 5.1. Block diagram of the proposed power control scheme with adaptive interference suppression. 231
Fig. 5.2. Performance comparison of the instantaneous MMSE power control scheme with different SIR estimation methods and the conventional power control scheme. ( , , ) 232
Fig. 5.3. Average SIR of all users versus power update index for the instantaneous MMSE power control scheme with different SIR estimation methods and the conventional power control scheme. ( , , ) 232
Fig. 5.4. Performance comparison of the instantaneous MMSE power control scheme with different SIR estimation methods and the adaptive MMSE power control scheme with various adaptive algorithms. ( , , ) 233
Fig. 5.5. Average SIR of all users versus power update index for the instantaneous MMSE power control scheme with different SIR estimation methods and the adaptive MMSE power control scheme with various adaptive algorithms. ( , , )
233
Fig. 5.6. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.4. ( , , ) 234
Fig. 5.7. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 235
Fig. 5.8. Average SIR of all users versus power update index for the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 235
Fig. 5.9. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.7. ( , , ) 236
Fig. 5.10. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 237
Fig. 5.11. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.10. ( , , ) 237
Fig. 5.12. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 238
Fig. 5.13. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.12. ( , , ) 238
Fig. 5.14. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 239
Fig. 5.15. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.14. ( , , ) 239
Fig. 5.16. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 240
Fig. 5.17. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.16. ( , , ) 240
Fig. 5.18. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 241
Fig. 5.19. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.18. ( , , ) 241
Fig. 5.20. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) Fig. 242
Fig. 5.21. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.20. ( , , ) 242
Fig. 5.22. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 243
Fig. 5.23. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.22. ( , , ) 243
Fig. 5.24. Performance comparison of the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , , ) 244
Fig. 5.25. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.24. ( , , ) 244
Fig. 5.26. Steady-state total transmit power versus the number of users for the instantaneous MMSE power control scheme and the adaptive MMSE power control scheme with different SIR estimation methods. ( , ) 245
Fig. 5.27. Robustness investigation of the adaptive MMSE power control scheme against various extents of channel gain estimation errors. ( , ) 246
Fig. 5.28. Block diagram of the proposed power control scheme with TPPIC. 247
Fig. 5.29. Performance investigation of the total-PIC power control scheme with different power update principles. ( , , , ) 248
Fig. 5.30. Average SIR of all users versus power update index for the total-PIC power control scheme with different power update principles. ( , , , )
248
Fig. 5.31. Performance comparison of various power control schemes. ( , , ) 249
Fig. 5.32. Average SIR of all users versus power update index for various power control schemes. ( , , ) 249
Fig. 5.33. Performance comparison of various power control schemes. ( , , ) 250
Fig. 5.34. Performance comparison of various power control schemes. ( , , ) 250
Fig. 5.35. Performance comparison of various power control schemes. ( , , ) 251
Fig. 5.36. Performance comparison of various power control schemes. ( , , ) 251
Fig. 5.37. Performance comparison of various power control schemes. ( , , ) 252
Fig. 5.38. Performance comparison of various power control schemes. ( , , ) 253
Fig. 5.39. Performance comparison of various power control schemes. ( , , ) 253
Fig. 5.40. Performance comparison of various power control schemes. ( , , ) 254
Fig. 5.41. Steady-state total transmit power versus the number of users for various power control schemes. ( , ) 255
Fig. 5.42. Total transmit power as a function of the target SIR for various power control schemes. ( , , ) 255
Fig. 5.43. Robustness investigation of the TPPIC power control scheme against various extents of channel gain estimation errors. ( , , ) 256
Fig. 5.44. Performance comparison of various power control schemes in a two-path indoor Rayleigh fading channel. ( , , , ) 257
Fig. 5.45. Average SIR of all users versus power update index for various power control schemes in a two-path indoor Rayleigh fading channel. ( , , , ) 257
Fig. 5.46. Performance comparison of various power control schemes in a two-path urban outdoor Rayleigh fading channel. ( , , , ) 258
Fig. 5.47. Average SIR of all users versus power update index for various power control schemes in a two-path urban outdoor Rayleigh fading channel. ( , , , ) 258
Fig. 5.48. Performance comparison of various power control schemes in a two-path rural outdoor Rayleigh fading channel. ( , , , ) 259
Fig. 5.49. Average SIR of all users versus power update index for various power control schemes in a two-path rural outdoor Rayleigh fading channel. ( , , , ) 259
Fig. 5.50. Block diagram of the proposed power control scheme using the combined TPPIC/AMMSE detector. 260
Fig. 5.51. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 261
Fig. 5.52. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 261
Fig. 5.53. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 262
Fig. 5.54. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 262
Fig. 5.55. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 263
Fig. 5.56. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 263
Fig. 5.57. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 264
Fig. 5.58. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 264
Fig. 5.59. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 265
Fig. 5.60. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 265
Fig. 5.61. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 266
Fig. 5.62. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 266
Fig. 5.63. Steady-state total transmit power versus the number of known users for various power control schemes. ( , , , ) 267
Fig. 5.64. Steady-state total transmit power versus the unknown user’s signal strength for various power control schemes. ( , , , ) 267
Fig. 5.65. Performance comparison of the adaptive MMSE power control scheme and the hybrid TPPIC/AMMSE power control scheme with different power control block lengths in a DS-CDMA system with unidentified users. ( , , , ) 268
Fig. 5.66. Total transmit power as a function of the target SIR for various power control schemes in a DS-CDMA system with unidentified users. ( , , , , ) 268
Fig. 5.67. Robustness investigation of the TPPIC/AMMSE power control scheme against various extents of channel gain estimation errors. ( , , , , ) 269
Fig. 5.68. Performance comparison of various power control schemes in a two-path indoor Rayleigh fading channel with existence of unidentified users. ( , , , , , ) 270
Fig. 5.69. Average SIR of all users versus power update index for various power control schemes in a two-path indoor Rayleigh fading channel with existence of unidentified users. ( , , , , , ) 270
Fig. 5.70. Performance comparison of various power control schemes in a two-path urban outdoor Rayleigh fading channel with existence of unidentified users. ( , , , , , ) 271
Fig. 5.71. Average SIR of all users versus power update index for various power control schemes in a two-path urban outdoor Rayleigh fading channel with existence of unidentified users. ( , , , , , ) 271
Fig. 5.72. Enlarged illustration of the simulation results shown in Fig. 5.70. ( , , , , , ) 272
Fig. 5.73. Performance comparison of various power control schemes in a two-path rural outdoor Rayleigh fading channel with existence of unidentified users. ( , , , , , ) 273
Fig. 5.74. Average SIR of all users versus power update index for various power control schemes in a two-path rural outdoor Rayleigh fading channel with existence of unidentified users. ( , , , , , )
273
Fig. 5.75. Enlarged illustration of the simulation results shown in Fig. 5.73. ( , , , , , ) 274
Fig. 5.76. Illustration of the power control approach in an NBI-existed environment. 275
Fig. 5.77. Performance comparison of various power control schemes in a DS-CDMA system under the narrowband jamming. ( , , ) 276
Fig. 5.78. Enlarged illustration of the steady-state convergence performance shown in Fig. 5.77. ( , , ) 276
Fig. 5.79. Performance comparison of various power control schemes in a DS-CDMA system with unidentified users and under the narrowband jamming. ( , , , ) 277
List of Tables
Table 3.1 Complexity Comparison for Various Approaches in and Evaluations. 141
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